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Call for papers
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Special Issue of Pattern Recognition
(The Journal of the Pattern Recognition Society)
WHAT IS INDUCTIVE LEARNING:
ON THE FOUNDATIONS OF PATTERN RECOGNITION, AI, AND COGNITIVE SCIENCE
Guest editor: Lev Goldfarb
Faculty of Computer Science
University of New Brunswick
Fredericton, N.B., Canada
The "shape" of AI (and, partly, of cognitive science), as it stands now,
has been molded largely by the three founding schools (at Massachusetts
institute of Technology, Carnegie-Mellon and Stanford Universities). This
"shape" stands now fragmented into several ill-defined research agendas
with no clear basic SCIENTIFIC problems (in the classical understanding of
the term) as their focus. It appears that four factors have contributed to
this situation: inability to focus on the central cognitive process(es),
lack of understanding of the structure of advanced scientific models,
failure to see the distinction between the computational/logical and
mathematical models, and the relative abundance of research funds for AI
during the last 35 years.
The resulting research agendas have prevented AI from cooperatively
evolving into a scientific discipline with some central "real" problems
that are inspired by the basic cognitive/biological processes. The
candidates for such basic processes could come only from the
central/common perceptual processes and only much later employed by the
"higher", e.g. language, processes: the period during which the "higher"
level processes have evolved is insignificant compared to that in which
the development of the perceptual processes took place (compare also the
anatomical development of the brain which does not show any basic changes
with the development of the "higher" processes).
Moreover, the partisan tradition in the development of AI may have also
inspired the recent "connectionist revolution" as well as other smaller
"revolutions", e.g., that related to the "genetic" learning. As a result,
in particular, even the most "reputable" connectionist histories of the
field of pattern recognition, which was formed more than three decades ago
and to which the connectionism properly belongs, show amazing ignorance of
the major developments in the parent (pattern recognition) field: the
emergence of two important and formally quite irreconcilable recognition
paradigms--vector space and syntactic. The latter ignorance is even more
instructive in view of the fact that many engineers who got involved with
the field of pattern recognition through the connectionist "movement" are
also ignorant of the above two paradigms that were discovered and
developed within largely applied/engineering parent field of pattern
recognition.
As far as the inception of a scientific field is concerned, it should be
quite clear that the initial choice of the basic scientific problem(s) is
of decisive importance. This is particularly true for cognitive modeling
where the path from the model to the experiment and the reverse path are
much more complex than was the case, for example, at the inception of
physics. In this connection, a very important question arises, which will
be addressed in the special issue: What form will the future/adequate
cognitive models take?
Furthermore, may be, as many cognitive scientists argue, since we are in a
prescientific stage, we should simply continue to collect more and more
data and not worry about the future models. The answer to the last
argument is quite clear to me: look very carefully at the "data" and the
corresponding experiments and you will note that no data can be even
collected without an underlying model, which always includes both formal
and informal components. In other words, we cannot avoid models
(especially in cognitive science, where the path from the model to the
experiment will be much longer and more complex than is the case in all
other sciences). Therefore, paraphrasing Friedrich Engels's thought on the
role of philosophy in science, one can say that there is absolutely no way
to do a scientific experiment without the underlying model and the
difference between a good scientist and a bad one has to do with the
degree to which each realizes this dependence and actively participates in
the selection of the corresponding model. It goes without saying that, at
the inception of the science, the decision on which cognitive process one
must focus initially should precede the selection of the model for the
process.
As to the choice of the basic scientific problem, or basic cognitive
process, it appears that the really central cognitive process is that of
inductive learning, which might have been marked so by many great
philosophers of the past four centuries (e.g., Bacon, Descartes, Pascal,
Locke, Hume, Kant, Mill, Russell, Quine) and even earlier (e.g.,
Aristotle). The insistence of such outstanding physiologists and
neurophysiologists as Helmholtz and Barlow on the central role of
inductive learning processes is also well known. However, in view of the
difficulties associated with developing an adequate inductive learning
model, researchers in AI and to a somewhat lesser extent in cognitive
science have decided to view inductive learning not as a central process
at all, i.e., they decided to "dissolve" the problem.
It became clear to me that the above difficulties are related to the
development of a genuinely new (symbolic) mathematical framework that can
SATISFACTORILY define the concept of INDUCTIVE CLASS REPRESENTATION (ICR),
i.e., the nature of encoding essentially infinite data set on the basis of
a small finite set. (The most known as well as critical to the development
of mathematics example of ICR is that of the classical Peano
representation of the set of natural numbers--one element plus one
operation--used in mathematical induction.) Thus, the main differences
between inductive learning models should be viewed in light of the
differences between the formal means, i.e. mathematical structures,
offered by various models for representing the class inductively. I will
also argue (in one of the papers) that the classical mathematical
(numeric) models, including the vector space and probabilistic models,
offer inadequate axiomatic frameworks for capturing the concept of ICR.
As Peter Gardenfors aptly remarked in his 1990 paper, "induction has been
called 'the scandal of philosophy' [and] unless more consideration is
given to the question of which form of knowledge representation is
appropriate for mechanized inductive inferences, I'm afraid that induction
may become a scandal of AI as well." I strongly believe that all attempts
to "dissolve" the inductive learning processes are futile and, moreover,
that these processes are central cognitive processes for all levels of
processing, hence the earlier workshop in Toronto (May 20-21) under the
same title and the present Special Issue.
I invite all researchers seriously interested in the scientific
foundations of cognitive science, AI, or pattern recognition to submit
the papers addressing, in addition to other relevant issues, the following
questions:
* What is the role of mathematics in cognitive science, AI,
and pattern recognition?
* Are there any central cognitive processes?
* What is inductive learning?
* What is inductive class representation (ICR)?
* Are there several basic inductive learning processes?
* Are the inductive learning processes central?
* What are the relations between inductive learning
processes and the known physical processes?
* What is the relationship between the measurement
processes and inductive learning processes (e.g., retina
as a structured measurement device)?
* What is the role of inductive learning in sensation
and perception (vision, hearing, etc.)?
* What is the relation between the inductive learning,
categorization, and pattern recognition?
* What is the relation between the supervised/inductive
learning and the unsupervised learning?
* What is the role of inductive learning processes in
language acquisition?
* What are the relationships, if any, between the inductive
class representation (ICR) and the basic object
representation (from the class)?
* What are the differences between the mathematical
structures employed by the known inductive learning
models for capturing the corresponding ICRs?
* What is the role of inductive learning in memory and
knowledge representation?
* What are the relations, if any, between the ICR and
mental models and frames?
When preparing the manuscript, please conform to the standard submission
requirements given in journal Pattern Recognition, which could be faxed or
mailed if necessary. Hardcopies (4) of each submission should be mailed
to
Lev Goldfarb
Faculty of Computer Science
University of New Brunswick
P.O. Box 4400 E-mail: goldfarb at unb.ca
Fredericton, N.B. E3B 5A3 Tel: 506-453-4566
Canada Fax: 506-453-3566
by the SUBMISSION DEADLINE, August 20, 1996. The review process should
take about 4-5 weeks and will take into account the relevance, quality,
and originality of the contribution. Potential contributors are encouraged
to contact me with any questions they might have.
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-- Lev Goldfarb
http://wwwos2.cs.unb.ca/profs/goldfarb/goldfarb.html